Map resource management system and method

Abstract

The application relates to the field of map resource management and discloses a map resource management system and method. The map resource management system comprises a driving environment acquisition module, a safety prediction module and a driving map generation module. The driving environment acquisition module acquires the driving environment of a driving section, wherein the driving environment comprises traffic participants and road conditions, the traffic participants comprise driving vehicles, riding vehicles and pedestrians, and the road conditions comprise building structures, road surface conditions and traffic signal lamp conditions. The safety prediction module creates a safety prediction model according to the driving environment and obtains a real-time safety prediction value. The driving map generation module generates a driving strategy with the highest safety according to the real-time safety prediction value, wherein the driving strategy comprises a driving state, a driving position and a driving speed, and generates a driving map based on the driving strategy. The application can improve the safety of vehicle driving.

Description

Technical Field

[0001] This invention relates to the field of map resource management, and specifically to a map resource management system and method. Background Technology

[0002] The vehicle-to-everything (V2X) big data information service system provides users with V2N-related data and information services, enhances the collaborative operation capabilities of intelligent connected vehicles and intelligent roadside equipment, thereby facilitating the large-scale application of "smart vehicles and smart roads," and also provides technical support for subsequent V2X operation and management. Map resource management is a crucial component for the local application of vehicle-road cooperative equipment.

[0003] Existing map resource management typically only manages map resources by collecting real-time information about road locations, the number of traffic lights, and traffic congestion. However, vehicle safety is affected by many factors during driving, and traditional map resource management can only provide navigation guidance. To further improve vehicle driving safety, it is urgent to upgrade map resource management. Summary of the Invention

[0004] The present invention aims to provide a map resource management system and method to improve the safety of vehicle driving.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Map resource management system, including:

[0007] The driving environment acquisition module acquires the driving environment of the driving segment. The driving environment includes traffic participants and road conditions. The traffic participants include driving vehicles, cycling vehicles, and pedestrians. The road conditions include building structures, road surface conditions, and traffic light conditions.

[0008] The safety prediction module creates a safety prediction model based on the driving environment and obtains real-time safety prediction values.

[0009] The driving map generation module generates the safest driving strategy based on real-time safety prediction values. The driving strategy includes driving state, driving position, and driving speed, and generates a driving map based on the driving strategy.

[0010] The principles and advantages of this solution are as follows: In practical applications, the driving environment acquisition module acquires traffic participants and road conditions for the driving segment; the safety prediction module creates a safety prediction model based on the driving environment to obtain real-time safety prediction values; through these real-time safety prediction values, the safety of the driving process can be quantified, serving as a warning to drivers; the driving map generation module generates the safest driving strategy based on the real-time safety prediction values, including driving state, driving position, and driving speed, and generates a driving map based on the driving strategy; intelligent generation of driving maps can improve driver safety; in addition, this system can be applied to autonomous driving to improve vehicle intelligence; and generating driving maps by acquiring the driving environment of the driving segment in real time is also of great significance for coordinating traffic congestion.

[0011] Preferably, as an improvement, the security prediction model is:

[0012]

[0013] Where A is the real-time security prediction value, and R i For the complexity of traffic participants in driving segment i, D i For driving segment i, J i Let k be the visibility of driving segment i, and k be a constant. This is the safety adjustment coefficient for driving segment i.

[0014] Technical effect: During vehicle driving, safety is affected by many factors. By constructing a safety prediction model, safety can be quantified and real-time safety prediction values ​​can be obtained, thereby improving the alertness to drivers.

[0015] Preferably, as an improvement, the driving map generation module further includes:

[0016] The driving location acquisition submodule acquires multiple reachable paths within the driving road segment and selects the reachable path with the highest real-time safety prediction value as the driving path;

[0017] The driving speed acquisition submodule determines the driving speed when there is an obstacle in front of the vehicle, provided that the driving speed meets the following conditions:

[0018] L=l0+v0t-v s t

[0019] Where L is the theoretical safe distance from the obstacle, l0 is the current distance from the obstacle, v0 is the speed of the obstacle, and v s Where t is the driving speed and t is the time.

[0020] When there are no obstacles in front of the vehicle, the driving speed satisfies:

[0021]

[0022] Among them, v x v represents the current speed limit for the road segment, A represents the real-time safety prediction value, and v is the speed limit for the current road segment. γ v is the velocity coefficient. l For v s Theoretical value;

[0023] The driving status acquisition submodule acquires the driving status based on the driving speed. The driving status includes starting, accelerating, constant speed, deceleration, stopping, and reversing.

[0024] The driving map generation submodule generates driving maps for driving routes based on driving strategies.

[0025] Technical benefits: Navigating a vehicle based on a driving map of the route can help drivers adapt to the real-time driving environment, thereby improving safety.

[0026] Preferably, as an improvement, the driving road segment is divided according to interference patterns. Specifically, each interference factor is identified, the number of each interference factor is counted, and the interference factors are scored. The scoring model is as follows:

[0027] P = G e *Q e +ΣG f *Q f

[0028] Among them, G e Q represents the number of interference factors. e G represents the score weight when the number of interference factors is e. f Q represents the number of class f interference factors. f The scoring weights for class f interference factors;

[0029] Based on the threshold range of the score, the interference mode is divided into one of the following: multi-interference mode, low-interference mode, slight-interference mode, or no-interference mode, and the driving road segment is divided according to the interference mode.

[0030] Technical benefits: Dividing driving segments by interference conditions facilitates accurate prediction of safety.

[0031] Preferably, as an improvement, it also includes:

[0032] The analysis module uses principal component analysis to analyze and save the driving speed at different times on the same road segment;

[0033] The delete module removes driving map data older than a preset time period.

[0034] Technical effect: It can clean up redundant data on a regular basis and retain valid data.

[0035] Map resource management methods include:

[0036] The driving environment acquisition step involves acquiring the driving environment of the driving segment. The driving environment includes traffic participants and road conditions. Traffic participants include vehicles, bicycles, and pedestrians. Road conditions include building structures, road surface conditions, and traffic light conditions.

[0037] The safety prediction step involves creating a safety prediction model based on the driving environment to obtain real-time safety prediction values.

[0038] The driving map generation step involves generating the safest driving strategy based on real-time safety prediction values. The driving strategy includes driving state, driving position, and driving speed, and then generating a driving map based on the driving strategy.

[0039] Preferably, as an improvement, the security prediction model in the security prediction step is:

[0040]

[0041] Where A is the real-time security prediction value, and R i For the complexity of traffic participants in driving segment i, D i For driving segment i, J i Let k be the visibility of driving segment i, and k be a constant. This is the safety adjustment coefficient for driving segment i.

[0042] Preferably, as an improvement, the driving map generation step further includes:

[0043] The driving location acquisition sub-step obtains multiple reachable paths within the driving road segment and selects the reachable path with the highest real-time safety prediction value as the driving path;

[0044] The driving speed acquisition sub-step, when there is an obstacle in front of the vehicle, the driving speed satisfies:

[0045] L=l0+v0t-v s t

[0046] Where L is the theoretical safe distance from the obstacle, l0 is the current distance from the obstacle, v0 is the speed of the obstacle, and v s Where t is the driving speed and t is the time.

[0047] When there are no obstacles in front of the vehicle, the driving speed satisfies:

[0048]

[0049] Among them, v x v represents the current speed limit for the road segment, A represents the real-time safety prediction value, and v is the speed limit for the current road segment. γ v is the velocity coefficient. l For v s Theoretical value;

[0050] The driving status acquisition sub-step acquires the driving status based on the driving speed. The driving status includes starting, accelerating, constant speed, decelerating, stopping, and reversing.

[0051] The driving map generation sub-step generates a driving map of the driving route based on the driving strategy.

[0052] Preferably, as an improvement, the driving road segments in the driving environment acquisition step are divided according to interference modes. Specifically, each interference factor is identified, the number of each interference factor is counted, and the interference factors are scored. The scoring model is as follows:

[0053] P = G e *Q e +ΣG f *Q f

[0054] Among them, G e Q represents the number of interference factors. e G represents the score weight when the number of interference factors is e. f Q represents the number of class f interference factors. f The scoring weights for class f interference factors;

[0055] Based on the threshold range of the score, the interference mode is divided into one of the following: multi-interference mode, low-interference mode, slight-interference mode, or no-interference mode, and the driving road segment is divided according to the interference mode.

[0056] Preferably, as an improvement, it also includes:

[0057] The analysis steps involve using principal component analysis to analyze and save the driving speeds at different times on the same road segment.

[0058] The deletion step deletes driving map data prior to a preset time period. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the structure of a map resource management system. Detailed Implementation

[0060] The following detailed description illustrates the specific implementation method:

[0061] The basic implementation examples are as follows: Figure 1 As shown:

[0062] Map resource management system, including:

[0063] The driving environment acquisition module acquires the driving environment of the driving segment. The driving environment includes traffic participants and road conditions. Traffic participants include vehicles, bicycles, and pedestrians. Vehicles include trucks, cars, buses, and vans, while bicycles include bicycles, motorcycles, tricycles, and electric bicycles. Road conditions include building structures, road surface conditions, and traffic light conditions. Building structures include road size, number of lanes, number of roundabouts, number of intersections, and number of forks. Road surface conditions include slope, potholes, and whether the road is concrete or asphalt. Traffic light conditions include location, number, and illumination time.

[0064] The driving road segment is divided according to interference patterns. Specifically, each interference factor is identified and its quantity is counted. In this embodiment, interference factors include road size, number of lanes, number of roundabouts, number of intersections, number of forks in the road, gradient, road surface potholes, concrete road, and number of traffic lights. The more interference factors there are, the more complex the driving road is, and the lower the safety. The interference factors are scored, and the scoring model is as follows:

[0065] P = G e *Q e +ΣG f *Q f

[0066] Among them, G e Q represents the number of interference factors. e G represents the score weight when the number of interference factors is e. f Q represents the number of class f interference factors. f The scoring weights for class f interference factors;

[0067] Based on the threshold range of the score, the interference mode is divided into one of the following: high interference mode, low interference mode, slight interference mode, or no interference mode. Driving segments are then divided according to the interference mode. By dividing driving segments according to interference mode, more accurate warnings can be provided to drivers.

[0068] The safety prediction module creates a safety prediction model based on the driving environment to obtain real-time safety prediction values; the safety prediction model is as follows:

[0069]

[0070] Where A is the real-time security prediction value, and R i For the complexity of traffic participants in driving segment i, D i For driving segment i, J iLet k be the visibility of driving segment i, and k be a constant. This is the safety adjustment coefficient for driving segment i.

[0071] When driving, a certain distance must be maintained from other road users. The more numerous and diverse the road users, the higher the complexity of the road user situation. Furthermore, different road users move at different speeds, resulting in a dynamic and changing landscape. Obtaining the complexity of road users allows for more accurate safety predictions. This complexity is determined based on the moving speed and direction of the road users. Specifically, it involves acquiring the moving speed and direction of road users within a preset range, identifying those moving towards the lane and those already within the lane. The more speed types and moving directions identified, the higher the complexity of the road user situation. Finally, the moving direction and speed are weighted to obtain the road user complexity value R. i .

[0072] When driving a vehicle, corresponding driving operations need to be performed according to road conditions. The more roundabouts, intersections, forks, and lanes there are, the higher the road complexity. Obtaining road complexity can more accurately predict safety. The road complexity is determined based on the number of travel directions for vehicles in each lane. Specifically, the number of lanes that vehicles can travel on all roads within a preset range is obtained. The larger the number of lanes, the higher the road complexity. The number of lanes multiplied by a preset complexity adjustment coefficient is the road complexity D. i .

[0073] Visibility refers to the maximum distance at which a person with normal vision can distinguish an object from its background. In other words, during the day, against a background of the sky near the horizon, one should be able to clearly see the outline of a dark object on the ground with an angle greater than 20 degrees and identify what it is. At night, one should be able to clearly see the luminous point of a target light. The magnitude of visibility is mainly determined by two factors: ① The difference in brightness between the target object and the background. The greater the difference, the greater the visibility distance; this difference in brightness usually does not change much. ② Atmospheric transparency. The air layer between the observer and the target object can reduce the aforementioned difference in brightness. The better the atmospheric transparency, the greater the visibility distance. Therefore, changes in visibility mainly depend on the quality of atmospheric transparency. Weather phenomena such as fog, smoke, dust storms, heavy snow, and drizzle can make the atmosphere turbid and reduce transparency. According to aviation requirements, visibility can be divided into two categories: ① Ground visibility, which reflects the horizontal visibility distance near the ground. Visibility in different directions often varies. Meteorological stations typically report a representative value of visibility, known as "effective visibility" (the visible distance that can be reached within more than half of the station's field of view). The visibility in this application is primarily affected by weather phenomena; visibility J... i The maximum observable distance in the lane is multiplied by a preset visibility adjustment coefficient.

[0074] The driving map generation module generates the safest driving strategy based on real-time safety prediction values. The driving strategy includes driving state, driving position, and driving speed, and generates a driving map based on the driving strategy.

[0075] The driving map generation module also includes:

[0076] The driving location acquisition submodule acquires multiple reachable paths within the driving road segment and selects the reachable path with the highest real-time safety prediction value as the driving path;

[0077] The driving speed acquisition submodule determines the driving speed when there is an obstacle in front of the vehicle, provided that the driving speed meets the following conditions:

[0078] L=l0+v0t-v s t

[0079] Where L is the theoretical safe distance from the obstacle, l0 is the current distance from the obstacle, v0 is the speed of the obstacle, and v s Where t is the driving speed and t is the time.

[0080] When there are no obstacles in front of the vehicle, the driving speed satisfies:

[0081]

[0082] Among them, v x v represents the current speed limit for the road segment, A represents the real-time safety prediction value, and v is the speed limit for the current road segment. γ v is the velocity coefficient. l For v s Theoretical value;

[0083] The driving status acquisition submodule acquires the driving status based on the driving speed. The driving status includes starting, accelerating, constant speed, deceleration, stopping, and reversing.

[0084] The driving map generation submodule generates driving maps for driving segments based on driving strategies. Navigating the vehicle using these driving maps helps drivers consider the real-time driving environment, thereby improving safety.

[0085] It also includes an analysis module that uses principal component analysis to analyze and save driving speeds at different times on the same road segment; and a deletion module that deletes driving map data older than a preset time period. Redundant data is periodically cleaned up, retaining only valid data.

[0086] Map resource management methods include:

[0087] The driving environment acquisition step involves acquiring the driving environment of the driving segment. The driving environment includes traffic participants and road conditions. Traffic participants include vehicles, bicycles, and pedestrians. Road conditions include building structures, road surface conditions, and traffic light conditions.

[0088] The driving environment acquisition step divides the driving road segments according to interference patterns. Specifically, it identifies each interference factor, counts the number of each interference factor, and scores the interference factors using a scoring model:

[0089] P = G e *Q e +ΣG f *Q f

[0090] Among them, G e Q represents the number of interference factors. e G represents the score weight when the number of interference factors is e. f Q represents the number of class f interference factors. f The scoring weights for class f interference factors;

[0091] Based on the threshold range of the score, the interference mode is divided into one of the following: multi-interference mode, low-interference mode, slight-interference mode, or no-interference mode, and the driving road segment is divided according to the interference mode.

[0092] The safety prediction step involves creating a safety prediction model based on the driving environment to obtain real-time safety prediction values.

[0093] The security prediction model in the security prediction step is:

[0094]

[0095] Where A is the real-time security prediction value, and R i For the complexity of traffic participants in driving segment i, D i For driving segment i, J i Let k be the visibility of driving segment i, and k be a constant. This is the safety adjustment coefficient for driving segment i.

[0096] The driving map generation step involves generating the safest driving strategy based on real-time safety prediction values. This driving strategy includes driving state, driving position, and driving speed, and then generating a driving map based on this strategy. The driving map generation step further includes:

[0097] The driving location acquisition sub-step obtains multiple reachable paths within the driving road segment and selects the reachable path with the highest real-time safety prediction value as the driving path;

[0098] The driving speed acquisition sub-step, when there is an obstacle in front of the vehicle, the driving speed satisfies:

[0099] L=l0+v0t-v s t

[0100] Where L is the theoretical safe distance from the obstacle, l0 is the current distance from the obstacle, v0 is the speed of the obstacle, and v s Where t is the driving speed and t is the time.

[0101] When there are no obstacles in front of the vehicle, the driving speed satisfies:

[0102]

[0103] Among them, v x v represents the current speed limit for the road segment, A represents the real-time safety prediction value, and v is the speed limit for the current road segment. γ v is the velocity coefficient. l For v s Theoretical value;

[0104] The driving status acquisition sub-step acquires the driving status based on the driving speed. The driving status includes starting, accelerating, constant speed, decelerating, stopping, and reversing.

[0105] The driving map generation sub-step generates a driving map of the driving route based on the driving strategy.

[0106] It also includes: an analysis step, which uses principal component analysis to analyze and save driving speeds at different times on the same road segment; and a deletion step, which deletes driving map data from a preset time period.

[0107] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A map resource management system, characterized by, include: The driving environment acquisition module acquires the driving environment of the driving segment. The driving environment includes traffic participants and road conditions. The traffic participants include driving vehicles, cycling vehicles, and pedestrians. The road conditions include building structures, road surface conditions, and traffic light conditions. The safety prediction module creates a safety prediction model based on the driving environment to obtain real-time safety prediction values; the safety prediction model is as follows: in, This is a real-time safety prediction value. For the complexity of traffic participants in driving segment i, Given the road complexity of driving segment i, Let k be the visibility of driving segment i, and k be a constant. This is the safety adjustment coefficient for driving segment i; The driving map generation module generates the safest driving strategy based on real-time safety prediction values. The driving strategy includes driving status, driving position, and driving speed, and generates a driving map based on the driving strategy. The driving map generation module also includes: The driving location acquisition submodule acquires multiple reachable paths within the driving road segment and selects the reachable path with the highest real-time safety prediction value as the driving path; The driving speed acquisition submodule determines the driving speed when there is an obstacle in front of the vehicle, provided that the driving speed meets the following conditions: in, This is the theoretical safe distance from the obstacle. The current distance to the obstacle. The speed of the obstacle's movement. Where t is the driving speed and t is the time. When there are no obstacles in front of the vehicle, the driving speed satisfies: in, This is the current speed limit for this road section. This is a real-time safety prediction value. For speed coefficient, for Theoretical value; The driving status acquisition submodule acquires the driving status based on the driving speed. The driving status includes starting, accelerating, constant speed, decelerating, stopping, and reversing. The driving map generation submodule generates driving maps for driving routes based on driving strategies. The driving sections are divided according to interference patterns. Specifically, each interference factor is identified, the number of each interference factor is counted, and the interference factors are scored. The scoring model is as follows: in, The number of interfering factors. The scoring weight is e when the number of interference factors is e. The number of class f interference factors, The scoring weights for class f interference factors; Based on the threshold range of the score, the interference mode is divided into one of the following: multi-interference mode, low-interference mode, micro-interference mode, or no-interference mode, and the driving road segment is divided according to the interference mode.

2. The map resource management system according to claim 1, characterized in that, Also includes: The analysis module uses principal component analysis to analyze and save the driving speed at different times on the same road segment; The delete module removes driving map data older than a preset time period.

3. A map resource management method, characterized in that, include: The driving environment acquisition step involves acquiring the driving environment of the driving segment. The driving environment includes traffic participants and road conditions. Traffic participants include vehicles, bicycles, and pedestrians. Road conditions include building structures, road surface conditions, and traffic light conditions. The safety prediction step involves creating a safety prediction model based on the driving environment to obtain real-time safety prediction values; the safety prediction model in this step is: in, This is a real-time safety prediction value. For the complexity of traffic participants in driving segment i, Given the road complexity of driving segment i, Let k be the visibility of driving segment i, and k be a constant. This is the safety adjustment coefficient for driving segment i; The driving map generation step involves generating the safest driving strategy based on real-time safety prediction values. The driving strategy includes driving state, driving position, and driving speed, and generating a driving map based on the driving strategy. The driving map generation step also includes: The driving location acquisition sub-step obtains multiple reachable paths within the driving segment and selects the reachable path with the highest real-time safety prediction value as the driving path; The driving speed acquisition sub-step, when there is an obstacle in front of the vehicle, the driving speed satisfies: in, This is the theoretical safe distance from the obstacle. The current distance to the obstacle. The speed of the obstacle's movement. Where t is the driving speed and t is the time. When there are no obstacles in front of the vehicle, the driving speed satisfies: in, This is the current speed limit for this road section. This is a real-time safety prediction value. For speed coefficient, for Theoretical value; The driving status acquisition sub-step acquires the driving status based on the driving speed. The driving status includes starting, accelerating, constant speed, decelerating, stopping, and reversing. The driving map generation sub-step generates a driving map of the driving route based on the driving strategy. The driving environment acquisition step divides the driving road segments according to interference patterns. Specifically, it identifies each interference factor, counts the number of each interference factor, and scores the interference factors using a scoring model: in, The number of interfering factors. The scoring weight is e when the number of interference factors is e. The number of class f interference factors, The scoring weights for class f interference factors; Based on the threshold range of the score, the interference mode is divided into one of the following: multi-interference mode, low-interference mode, micro-interference mode, or no-interference mode, and the driving road segment is divided according to the interference mode.

4. The map resource management method according to claim 3, characterized in that, Also includes: The analysis steps involve using principal component analysis to analyze and save the driving speeds at different times on the same road segment. The deletion step deletes driving map data prior to a preset time period.

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